Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Anatomy of the Eyeball

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...

The Retina

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

AdapHBNA: Adaptive hierarchical spatio-temporal brain network analysis for brain disease detection.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Hollow-core fiber gas lasers [Invited].

Light, science & applications·2026

Same author

Detection of diabetic retinopathy using multicolor image by multimodal network incorporating information bottleneck (MNIIB).

Scientific reports·2025

Same author

Tumor and Perirenal Adipose Tissue Radiomic Models for Pathological T-Stage Prediction and Biological Exploration in Clear Cell Renal Cell Carcinoma.

European journal of radiology·2025

Same author

Off-OAB: Off-Policy Policy Gradient Method With Optimal Action-Dependent Baseline.

IEEE transactions on neural networks and learning systems·2025

Same author

Comprehensive investigation of longitudinal spindle-shaped Yb-doped fibers for high-power laser applications.

Optics express·2025

Search research articles

Related Experiment Video

Updated: May 9, 2026

Retrograde Labeling of Retinal Ganglion Cells in Adult Zebrafish with Fluorescent Dyes

Retrograde Labeling of Retinal Ganglion Cells in Adult Zebrafish with Fluorescent Dyes

Published on: May 3, 2014

Retinal identification based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform.

Xianjing Meng¹, Yilong Yin, Gongping Yang

¹School of Computer Science and Technology, Shandong University, Jinan 250101, China. rongmengyuan@gmail.com

Sensors (Basel, Switzerland)

|July 23, 2013

Summary

Retinal identification uses unique vascular patterns for secure authentication. A new Improved Circular Gabor Transform (ICGF) preprocessing method enhances Scale Invariant Feature Transform (SIFT) accuracy by reducing keypoint errors for robust identification.

More Related Videos

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Related Experiment Videos

Last Updated: May 9, 2026

Retrograde Labeling of Retinal Ganglion Cells in Adult Zebrafish with Fluorescent Dyes

Retrograde Labeling of Retinal Ganglion Cells in Adult Zebrafish with Fluorescent Dyes

Published on: May 3, 2014

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Area of Science:

Biometrics and Pattern Recognition
Image Processing and Computer Vision

Background:

Retinal identification offers highly secure authentication using unique vascular patterns.
Existing Scale Invariant Feature Transform (SIFT) methods for retinal identification face challenges with feature extraction and matching accuracy.
Digital retinal images are susceptible to deformations, impacting biometric system performance.

Purpose of the Study:

To propose a novel preprocessing technique to improve the accuracy and robustness of SIFT-based retinal identification.
To address the limitations of feature extraction and mismatching in current SIFT applications for retinal biometrics.
To enhance the reliability of retinal identification systems against scale and rotation variations.

Main Methods:

A new preprocessing method based on the Improved Circular Gabor Transform (ICGF) was developed.
An iterated spatial anisotropic smooth method was applied to refine feature extraction.
The proposed ICGF preprocessing was integrated with the Scale Invariant Feature Transform (SIFT) algorithm.

Main Results:

The novel preprocessing method significantly reduced the number of uninformative SIFT keypoints.
The developed technique demonstrated promising results in retinal identification accuracy.
The method showed robustness against rotational and scale changes in retinal images.

Conclusions:

The Improved Circular Gabor Transform (ICGF) combined with spatial anisotropic smoothing effectively enhances SIFT-based retinal identification.
This approach overcomes key limitations in feature extraction and matching, improving overall system performance.
The proposed method offers a robust solution for secure and accurate retinal authentication in biometric systems.